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Keywords:

  • Chief executive officer power;
  • Pay for performance sensitivity;
  • Firm performance;
  • Institutional investor concentration
  • G30;
  • G34;
  • J33;
  • L25;
  • M52;
  • G32

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Hypotheses Development
  5. 3. Empirical Design and Data
  6. 4. Pay for Performance Sensitivity
  7. 5. Firm Performance
  8. 6. Robustness
  9. 7. Conclusions
  10. References

This paper focuses on abnormal chief executive officer (CEO) structural power over top executives and examines its impacts on CEO pay for performance sensitivity and firm performance. We find that greater abnormal power is associated with weaker firm performance, but the relation is significant only when monitoring by external shareholders is weak. We also identify a channel through which the power adversely impacts firm performance: CEOs’ capture of the compensation process. Greater abnormal CEO power lowers CEOs’ pay for performance sensitivity, but again the relation is driven by observations under weak external monitoring. External monitoring is measured by institutional ownership concentration; the abnormal power, by residuals of a regression relating CEO structural power to its likely determinants. The negative impact of the abnormal power on firm performance is robust to potential reverse causality.


1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Hypotheses Development
  5. 3. Empirical Design and Data
  6. 4. Pay for Performance Sensitivity
  7. 5. Firm Performance
  8. 6. Robustness
  9. 7. Conclusions
  10. References

The influence of chief executive officers (CEOs) on corporate policies and performance has been well documented. Bertrand and Schoar (2003) find that CEO fixed effects matter for a wide range of firm policies; Bennedsen et al. (2006) document that CEO deaths are strongly negatively correlated with firm profitability and growth; Cronqvist et al. (2009) show that differences in corporate financial leverage can be traced to CEOs’ personal leverage; and Jenter and Lewellen (2011) provide evidence that CEO age approaching retirement has an important impact on the likelihood of their firms being taken over and on the takeover premiums that their shareholders receive.

These CEO impacts should be greater when CEOs have more power, where power is defined as the capacity to exert one’s own will on corporate decisions. Recent evidence on CEO power suggests that powerful CEOs are bad news for shareholders. Bebchuk et al. (2011) argue that concentration of power in the CEO reduces firm performance. Landier et al. (2008) show that strong CEO power over top executives hurts firm performance. Bertrand and Mullainathan (2000) demonstrate that in the absence of adequate monitoring by blockholders, CEOs manipulate the compensation process to pay themselves what they can. Bebchuk and Fried (2004) argue that powerful CEOs reduce the linkage between CEO compensation and firm performance. Morse et al. (2010) show that powerful CEOs rig incentive contracts.

With all these negative impacts of CEO power, why do firms grant power to CEOs? In an ideal world, shareholders would grant an optimal level of power, weighing various costs and benefits specific to a firm’s characteristics. For some firms, concentration of power in the CEO office helps to expedite decision-making processes, resulting in more timely and efficient reaction to internal and external problems or pro-action to anticipated changes in market conditions. Such benefits are evident in Adams et al. (2005), who find that powerful CEOs are associated with the best and the worst performances. We argue that the deleterious effects of CEO power arise from deviations from the optimal level. However, the deviation, abnormal CEO power, might not be bad; its desirability depends on how the power is used and whether its use is properly monitored and guided to protect and enhance shareholder value.

In the present paper, we focus on the deviation; namely, the abnormal power beyond the customary CEO power given CEO and firm characteristics. Specifically, we measure the abnormal CEO power by residuals of a regression relating a power measure to its likely determinants. This measure of abnormal power is related to a channel through which power might impact firm performance: CEOs’ pay for performance sensitivity (PPS). Our purpose is to examine whether power is used to capture the compensation process. We also consider whether the likelihood of the capture is affected by the intensity of monitoring by external shareholders. Because any capture of the compensation process is likely to adversely affect shareholder value, the final phase of our investigation focuses on the relation between the abnormal CEO power and firm performance, which is measured by Tobin’s Q or return on assets (ROA).

We find that abnormal CEO power is negatively related to PPS, and the negative relation is driven by observations under weak external monitoring (EM). The intensity of monitoring by external shareholders is measured by institutional ownership concentration (IOC). When IOC is high, CEO power has no effect on PPS or firm performance. Only when monitoring by the external shareholders is weak does the abnormal power reduce PPS and firm performance. When IOC is below the sample median, the median PPS for CEOs with the most abnormal power is lower by $0.164 per $1000 change in shareholder value than the median PPS for the CEO with the least abnormal power. Although $0.164 seems small in magnitude, it represents a 33% decrease in PPS relative to the PPS for the CEO with the least abnormal power. As for firm performance, when IOC is below the sample median, a one standard deviation increase in CEO abnormal power leads to a 0.342% decrease in Tobin’s Q and a 1.216% decrease in ROA. The sample mean Q and ROA are 2.148 and 4.098%, respectively.

These relations concerning firm performance and the abnormal power are subject to reverse causality, which we address by estimating three-stage least square regressions. Although the estimation results do not rule out the possibility that our measures of firm performance affect the abnormal power or IOC, our main conclusion is robust; namely, abnormal CEO power is bad for shareholder value when firms are subject to weak monitoring by external shareholders.

This study adds to the emerging literature on CEO impact and power. We use the measure of abnormal CEO structural power of Landier et al. (2008) and identify a negative relation between the power and firm performance that is robust to firm fixed characteristics and possible reverse causality. Furthermore, we demonstrate that the negative relation is at work only when monitoring by external shareholders is weak. When monitoring is strong, the abnormal CEO power seems benign. In addition, we reveal that CEOs’ PPS is a channel through which the power affects performance.

The rest of the paper is organized as follows. Section 2 develops hypotheses and defines the abnormal CEO power and EM. Section 3 describes our empirical design and data. Section 4 relates the abnormal power to PPS; and Section 5, to firm performance. Section 6 addresses endogeneity issues. Section 7 concludes.

2. Hypotheses Development

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Hypotheses Development
  5. 3. Empirical Design and Data
  6. 4. Pay for Performance Sensitivity
  7. 5. Firm Performance
  8. 6. Robustness
  9. 7. Conclusions
  10. References

This section presents hypotheses on how abnormal CEO power affects CEO pay for performance sensitivity (PPS) and firm performance. It also describes our measures of the abnormal power and the strength of monitoring by external shareholders. We begin with two non-mutually exclusive views of CEO power, the contracting and the capturing view.

2.1. Contracting and Capturing Views

In the contracting view, shareholders determine the optimal level of CEO power by trading off efficiency gains from having a more centralized CEO office against risks of abusing the power for private benefits. Shareholders also hold CEOs accountable for their actions through incentive contracts linking compensation to performance. However, asymmetric information and transaction costs in the market for CEOs might lead to deviations from optimal contracting. Some CEO characteristics are unknown to their employers at the time of hiring. When subsequent realizations reveal that the power granted to a CEO exceeds the optimal level, re-contracting is costly because the CEO is likely to resist relinquishing the abnormal power and replacing a CEO is expensive. These re-contracting costs arise because of investments required to acquire firm-specific CEO skills, uncertainty about the quality and availability of other suitable CEO candidates, and search costs, among others.

It is possible to reduce these costs ex-ante by initially giving a CEO a very low level of power and later adjusting it when his or her true characteristics are revealed. However, this strategy imposes other costs: interim efficiency losses during the adjustment process. When a firm is run by a CEO with abnormally weak influence, it might function like a firm run by committees. Such firms might not be able to efficiently react to or proactively manage changes in internal and external business environments in a timely manner. Furthermore, a qualified candidate with other comparable employment opportunities will not choose a CEO position that comes with unreasonably weak power, making the ex-ante solution untenable. Therefore, asymmetric information, re-contracting costs, interim efficiency, and competition between firms may lead to situations in which a CEO’s power deviates from the optimal level.

The positive deviations from the optimal level of CEO power allow for the capturing view, which is an extension of the skimming view articulated by Bertrand and Mullainathan (2000) and others (e.g. Crystal, 1991; Milbourn, 2003; Morse et al., 2010). The skimming view posits that when ownership is diffuse and without effective oversight by shareholders, powerful CEOs capture the compensation process and set their own pay. This view assumes that most CEOs would rather tilt the compensation process in their favor at the expense of firm performance. At any given point in time, the PPS reflects what remains after CEOs capture the governance process.

We define power as the capacity to exert one’s own will, as in Finkelstein (1992), who defines four dimensions of managerial power: structural power, ownership power, expert power, and prestige power. Structural power is based on organizational structure and hierarchical authority, both formal and informal.1 The aforementioned studies demonstrating risks of CEO power focus on this dimension of power.2 The focus of the present paper is also on CEO structural power.

2.2. Hypotheses

Our predictions are about how abnormal CEO structural power affects PPS and firm performance. We assume that CEOs prefer low PPS: they want more compensation for stronger performance and want to maintain the high level of compensation when performance is weak. To achieve this, CEOs might rig incentive contracts by altering the benchmark when performance is weak (Morse et al., 2010). Tweaking the performance benchmark would require other top executives’ support, which is easier to obtain when a CEO has strong structural power over his or her top executives. Hence, we predict:

Hypothesis 1.  Abnormal CEO power is negatively related to PPS.

We interpret the negative relation between abnormal CEO power and PPS as evidence of the abnormal power helping CEOs capture the governing process. When governance is captured by CEOs, their private benefits will receive higher priority than shareholder value maximization. Hence, we hypothesize:

Hypothesis 2.  Abnormal CEO power is negatively related to firm performance.

We also predict that the effects of CEO power on both PPS and firm performance depend on the strength of monitoring by external shareholders. Strong EM helps to curb the negative effects of abnormal CEO power. In our paper on CEO ownership (Kim and Lu, 2011), we find that both the benefits and deleterious effects of CEO ownership power are prevalent only when EM is weak. Therefore, we predict:

Hypothesis 3.  Effects of abnormal CEO power on PPS and firm performance are most pronounced when EM is too weak to preempt or restrain the negative effects of CEO power. Conversely, when EM is strong, the effects on PPS and performance are less noticeable.

3. Empirical Design and Data

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Hypotheses Development
  5. 3. Empirical Design and Data
  6. 4. Pay for Performance Sensitivity
  7. 5. Firm Performance
  8. 6. Robustness
  9. 7. Conclusions
  10. References

3.1. Proxy for Chief Executive Power

We measure the abnormal CEO structural power by the excess fraction of top executives hired by a CEO, as in Landier et al. (2008). CEOs are heavily involved in recruiting their top lieutenants; consequently, top executives during a CEO’s tenure are more likely to share similar preferences and to be more loyal to the CEO. These executives are less likely to dissent, whereas executives accustomed to working with a previous CEO are more likely to challenge orders received from a new CEO. Therefore, we assume the greater the fraction of top executives hired by a CEO, the greater the CEO’s informal hierarchical authority and, hence, the stronger is his or her structural power over the executives.

The fraction of top executives hired during a CEO’s tenure tends to be related to a number of factors, such as the tenure of the CEO and of other top executives and whether the CEO is recruited from outside. Therefore, we follow Landier et al. and measure the abnormal fraction hired by CEOs with residuals of the following regression:

  • image(1)

Here, Frac is the fraction of executives hired by the CEO; CEOTEN, CEO’s tenure (in years);3EXECSEN, average non-CEO executive tenure; OUTSIDE, an indicator variable equal to one if the CEO comes from outside the firm; KNOWN, the fraction of executives for which tenure is reported in the data; and FRAC_1Y, the fraction of executives who arrived within a year of the CEO’s nomination. The regression also controls for year fixed effects to account for macroeconomic factors affecting top executive hiring decisions. Table 1 contains definitions of all variables.

Table 1.   Definitions of variables used in performance and pay-for-performance-sensitivity analyses
Variable
Panel A: Variables used in performance analyses
PowerAbnormal fraction of executives hired by the firm during the current CEO’s tenure. The internal rank of executives is based on the sum of salaries and bonuses
Tobin’s QThe market value of common equity plus the book value of total liabilities divided by the book value of total assets
ROAReturn on total assets
FirmAgeLog of one plus the listing age of a firm measured by the number of years from the firm’s initial public offering as reported in CRSP or the number of years since its first appearance in CRSP
LNSLog (Sales)
K/SThe ratio of property, plant, and equipment to sales
I/KThe ratio of capital expenditures to property, plant, and equipment
FemaleIndicator equal to one if a CEO is female, and zero otherwise
CEOAgeLog(CEO age)
CEO_ChairIndicator equal to one if a CEO also chairs the board, and zero otherwise
IOCThe sum of percentage share ownership of the top five institutional investors
IOC_IY(−i)The mean value of IOC of all firms in the same industry in a given year, excluding firm i itself
Panel B: Variables used in pay-for-performance-sensitivity analyses
Ch_Flow_CEO_Com ($K)The change in total direct CEO compensation flow (including salaries, bonuses, options, stock grants, and other compensation) in 2000 dollars from year t − 1 to year t
Ch_sv ($MM)The change in shareholder value, measured by the product of shareholder value in 2000 dollars at year t − 2 and the geometric mean of shareholder rate of returns from t − 2 to t
CDF(Power)The cumulative density function of the power variable
CDF(CEO Age)The cumulative density function of the current age of the CEO
CDF(Firm Size)The cumulative density function of log total assets
CDF(Risk)The cumulative density function of the variance of daily stock returns during the current year

The residuals in the regression may be viewed as abnormal fractions of executives hired by CEOs. This is our proxy for abnormal CEO structural power over other top executives.4Table 2 shows the regression result. Consistent with Landier et al. (2008), the fraction of executives appointed during a CEO’s tenure is positively related to the length of CEO tenure and negatively related to the average non-CEO executive tenure. A substantial fraction of new hiring seems to be done during a CEO’s first year in office, and a CEO hired from outside tends to hire fewer top executives during his or her tenure.

Table 2.   Regression to construct abnormal fraction (Frac) of top-executives hired during a chief executive officer’s (CEO’s) tenure This table reports regression estimates to construct the abnormal fraction of top executives hired during a CEO’s tenure. Power variable is the residual of the regression in which the dependent variable is the percentage of top executives hired by the firm during a CEO’s tenure. The regression controls for year fixed effects. Robust standard errors are in parentheses. Coefficients marked with ***, **, and * are significant at 1, 5, and 10%, respectively.
 Frac (1)
CEOTEN0.002*** (0.000)
EXECSEN−0.001*** (0.000)
OUTSIDE−0.011*** (0.002)
KNOWN−0.007* (0.004)
FRAC_1Y0.835*** (0.005)
Constant−0.012*** (0.003)
Year fixed effectsY
Observations20 730
Adjusted R20.83

3.2. Strength of External Monitoring

Our proxy for the strength of EM is IOC. Previous researchers demonstrate the important monitoring role of institutional investors and block holders in shaping corporate governance (e.g. Shleifer and Vishny, 1986; Bertrand and Mullainathan, 2000, 2001; Hartzell and Starks, 2003; Cremers and Nair, 2005; Del Guercio et al., 2008; Edmans, 2009). We follow Hartzell and Starks (2003) and estimate IOC by the percentage of institutional holdings by top five institutions. The results are robust to measuring IOC by the Herfindahl Index of institutional ownership. Institutional ownership data is obtained from the CAS Spectrum database.

3.3. Data and Summary Statistics

We use panel data from 1994 through 2006, constructed by merging the executive data in ExecuComp with accounting data in Compustat and stock return data in CRSP. We drop firm-year observations in which a new CEO’s first year in office overlaps with the last year of the previous CEO. Our total sample consists of 11 474 firm-year observations associated with 1397 unique firms over the period 1994–2006. Table 3 shows the number of observations by year for the full sample, and separately for high IOC and low IOC. High IOC (HIOC) and Low IOC (LIOC) are defined as those with above and below the sample median IOC.5 The number of LIOC observations is greater (smaller) than HIOC observations in the earlier (later) years because of the steady increase in IOC over time. Sample size for individual regressions varies depending on the availability of data to construct dependent and independent variables.

Table 3.   Number of observations by year This table shows the number of observations by year. Column (2) reports the number of firms in the full sample by year. Columns (3) and (4) report the number of firms in the HIOC and LIOC sample, respectively. IOC is defined as the sum of percentage share ownership of the top 5 institutional investors. HIOC and LIOC are defined as above and below the sample median IOC.
Year (1)Full (2)HIOC (3)LIOC (4)
1994577154283
1995610189293
1996645191327
1997692253309
1998765286343
1999867339382
2000930389398
2001928406401
2002963463398
20031031457458
20041082614361
20051130691336
20061254755307
Total11 47451874596

Table 4 provides summary statistics for key variables. The average fraction of top four executives hired during a CEO’s tenure is 15.6%. Importantly, the mean and median of our proxy for abnormal CEO power are close to zero. A number of variables, especially changes in CEO compensation flow and shareholder value, indicate the presence of large outliers even after winsorizing them at 1 and 99%. We are mindful of these outliers and design our estimation of PPS to mitigate the outlier problems. The difference in the number of observations across variables is due to missing variables.6

Table 4.   Summary statistics for all variables used in performance and pay-for-performance-sensitivity analyses Definitions of the variables are given in Table 1. Panel A reports the summary statistics for the variables used in performance analyses. Panel B reports the summary statistics for the variables used in pay-for-performance sensitivity analyses.
VariableObservation (1)Mean (2)Median (3)SD (4)Minimum (5)Maximum (6)
Panel A: Variables used in performance analyses
FRAC11 4740.1560.0000.2480.0001.000
Power11 479−0.0030.0100.106−0.8400.191
Tobin’s Q11 4792.1481.5482.5600.398105.090
ROA10 7884.0984.89616.825−577.850233.264
FirmAge11 4232.8802.9960.8970.0004.407
LNS10 7837.3127.2221.564−2.27912.578
K/S10 4680.8830.4512.9230.000244.333
I/K10 3430.1250.0980.1030.0004.302
Female11 4790.0140.0000.1190.0001.000
CEOAge10 9204.0144.0250.1383.3674.511
CEO_Chair11 4790.6461.0000.4780.0001.000
IOC97830.2590.2510.1040.0000.972
IOC_IY(−i)95650.2610.2630.0410.1070.415
Panel B: Variables used in pay-for-performance sensitivity analyses
Ch_Flow_CEO_Com ($K)10 740226.42491.2502194.579−9492.2849514.891
Ch_sv ($MM)13 091444.756113.2941611.402−7118.04113 868.280
CDF(Power)85930.5030.5040.2930.0001.000
CDF(CEO Age)13 1700.5510.6000.2840.0001.000
CDF(Firm Size)13 9600.5330.5440.2790.0001.000
CDF(Risk)14 7240.4880.4810.2850.0001.000

4. Pay for Performance Sensitivity

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Hypotheses Development
  5. 3. Empirical Design and Data
  6. 4. Pay for Performance Sensitivity
  7. 5. Firm Performance
  8. 6. Robustness
  9. 7. Conclusions
  10. References

In this section, we examine whether abnormal CEO power helps CEOs capture the compensation process by relating changes in total CEO compensation flows to changes in shareholder value. Our specification closely resembles those of Hartzell and Starks (2003), Milbourn (2003), and Rajgopal et al. (2006). We estimate:

  • image(2)

Here, ΔTotal Compensationit is the change in total direct compensation flow (including salaries, bonuses, value of new stocks and stock options granted, and other compensation) to the CEO of firm i in 2000 dollars from year − 1 to year t. It is winsorized at the 1 and 99 percentile. ΔShareholder Valueit is changes in shareholder value from − 2 to t, equal to shareholder value in 2000 dollars at − 2 multiplied by the geometric mean rates of returns from − 2 to t. We use the mean shareholder returns over 2 years to allow CEO compensation to reflect performance not only during the concurrent year but also during the prior year.

Control variables include: firm size, as measured by log of total assets, to account for the well-documented relation between firm size and PPS (e.g. Schaefer, 1998; Baker and Hall, 2004); risk measured by the variance of stock return to account for a possible correlation between the uncertainty surrounding the firm and output based pay (Lafontaine and Bhattacharyya, 1995; Aggarwal and Samwick, 1999; Prendergast, 2002); and CEO age because it is shown to be related to PPS (Gibbons and Murphy, 1992; Milbourn, 2003). Power and control variables are lagged by 1 year. Because ΔTotal Compensation is calculated based on the change in total direct compensation flow of the CEO from − 1 to year t, we drop firm-year observations in which a new CEO’s first year in office overlaps with the last year of the previous CEO’s tenure.

We follow Aggarwal and Samwick (1999) and Milbourn (2003) and use cumulative density functions for variables interacted with shareholder value change. Using cumulative density functions reduces the importance of outliers by normalizing the variables to the unit interval. It also helps to interpret the estimated coefficients β1 and β2 in an economically meaningful way, revealing how the power affects PPS.

We estimate two regressions: equation (1), a median regression with industry fixed effects to account for industry differences in PPS (Murphy, 1999), where industry is defined by the Fama–French 48 industry groupings; and equation (2), an OLS regression with firm fixed effects.7 Both regressions include year fixed effects. Median regressions are also used by Hall and Liebman (1998), Milbourn (2003), and Rajgopal et al. (2006). They are more robust to the presence of large outliers than mean regressions. This robustness is important because summary statistics in Table 4 indicate the presence of large outliers in changes of CEO compensation flow and of shareholder value even after winsorizing. The precision of estimates from a median regression is higher, because the median regression is a more robust estimate of central tendency than the mean regression.8

4.1. Full Sample results

Table 5 reports the regression estimates relating abnormal CEO power to PPS. The coefficients of interest are those on the interaction terms, especially the interaction between shareholder value changes and the power variable. A positive coefficient means abnormal CEO power increases PPS; a negative coefficient means abnormal power decreases PPS.

Table 5.   Relation between chief executive officer (CEO) power and pay-for-performance sensitivity Regression in column (1) is estimated by using the median regressions with year and industry fixed effects. Industries are defined as in Fama and French (1997). The regression in column (2) is estimated by using the OLS regression with year and firm fixed effects. Definitions of all variables are given in Table 1. The sample is constructed by excluding the year of CEO turnover and the following year. Standard errors are reported in parentheses. Coefficients marked with ***, **, and * are significant at 1, 5, and 10%, respectively.
 Ch_Flow_CEO_Com ($K)
Median regression (1)OLS regression (2)
Ch_sv ($MM)0.694*** (0.047)0.870*** (0.178)
Ch_sv ($MM) × CDF(Power)t−1−0.040* (0.021)−0.056 (0.082)
CDF(Power)t−146.861* (27.017)−78.968 (133.716)
Ch_sv ($MM) × CDF(CEO Age)t−1−0.039* (0.023)−0.135 (0.093)
CDF(CEO Age)t−1−63.310** (29.178)−138.121 (158.593)
Ch_sv ($MM) × CDF(Firm Size)t−1−0.597*** (0.045)−0.637*** (0.171)
CDF(Firm Size)t−1194.600*** (37.328)−1282.532*** (436.178)
Ch_sv ($MM) × CDF(Risk)t−10.164*** (0.026)−0.017 (0.097)
CDF(Risk)t−166.017 (40.592)88.457 (243.197)
Constant−312.524 (265.055)1031.348*** (315.396)
Firm fixed effects and year fixed effectsNY
Industry fixed effects and year fixed effectsYN
Observations96499649
Adjusted R2 (Pseudo R2)(0.0226)−0.06

Column (1), using the median regression, indicates that abnormal power has a negative and significant impact on PPS. The OLS estimation result in Column (2) also shows a negative but insignificant coefficient. The overall evidence suggests that abnormal power helps CEOs capture the compensation process; however, the statistical significance is sensitive to the choice of model specification.

Results on control variables are consistent with previous findings: PPS is negatively related to firm size (Schaefer, 1998; Baker and Hall, 2004), and the median regression shows a positive relation between PPS and risk, which is consistent with Rajgopal et al. (2006) and Morse et al. (2010). According to Lafontaine and Bhattacharyya (1995) and Prendergast (2002), firms with low uncertainty are easier to monitor and, hence, are less reliant on pay incentives.

4.2. Interactive Effects with External Monitoring

The results based on the full sample mask important heterogeneity. The relation between abnormal CEO power and PPS might be stronger for firms under weaker EM, which makes it easier for CEOs to use their power to influence the compensation process. Strong EM, in contrast, might make it difficult to capture the compensation process. To test this hypothesis, we separate firm-year observations into strong and weak EM subsamples. An observation is considered to be under strong (weak) EM if its IOC is above (below) the sample median. Separate estimation for each subsample allows for coefficients of the independent variables and fixed effects to vary across strong and weak EM regimes.

Table 6 reports the subsample results. The results reveal no relation between abnormal power and PPS when EM is strong (odd numbered columns), but a significant negative relation when EM is weak (even numbered columns). This is true regardless of whether we use the median regression specification or OLS with firm fixed effects. The negative relation between abnormal power and PPS is driven by observations under weak EM.

Table 6.   Relation between chief executive officer (CEO) power and pay-for-performance sensitivity: high and low institutional ownership concentration This table reports regression estimates separately for firm year observations with high and low institutional ownership concentration (IOC). Columns (1) and (3) report the results estimated for the HIOC subsample, columns (2) and (4) report the results estimated for the LIOC subsample. HIOC and LIOC are defined as above and below the sample median IOC. Regressions in columns (1) and (2) are estimated by using median regressions with year and industry fixed effects. Industries are defined as in Fama and French (1997). Regressions in columns (3) and (4) are estimated by using the OLS regression with year and firm fixed effects. Definitions of all variables are given in Table 1. All regressions control for firm and year fixed effects. Robust standard errors are reported in parentheses. Coefficients marked with ***, **, and * are significant at 1, 5, and 10%, respectively.
 Ch_Flow_CEO_Com ($K)
Median regressionOLS regression
HIOC (1)LIOC (2)HIOC (3)LIOC (4)
Ch_sv ($MM)0.931*** (0.111)0.497*** (0.069)1.506*** (0.347)0.626** (0.255)
Ch_sv ($MM) × CDF(Power)t−10.008 (0.058)−0.164*** (0.030)−0.074 (0.186)−0.250** (0.110)
CDF(Power)t−1−21.081 (51.720)62.916 (48.225)−228.736 (223.568)−64.690 (240.737)
Ch_sv ($MM) × CDF(CEO Age)t−1−0.215*** (0.061)0.043 (0.033)−0.413** (0.186)−0.080 (0.137)
CDF(CEO Age)t−1−83.389 (57.356)3.837 (50.594)−607.148** (278.996)31.904 (270.143)
Ch_sv ($MM) × CDF(Firm Size)t−1−0.783*** (0.102)−0.339*** (0.064)−1.150*** (0.323)−0.275 (0.246)
CDF(Firm Size)t−163.311 (74.107)193.368*** (65.179)−1808.376** (749.624)−1280.067 (836.975)
Ch_sv ($MM) × CDF(Risk)t−10.375*** (0.069)−0.013 (0.036)0.100 (0.208)−0.262* (0.136)
CDF(Risk)t−179.688 (79.371)−45.811 (73.148)592.502 (411.806)−25.631 (458.024)
Constant−622.874 (473.333)156.463 (343.789)687.201 (427.038)572.442 (497.382)
Firm fixed effects and year fixed effectsNN YY
Industry fixed effects and year fixed effectsYY NN
Observations4281360842813608
Adjusted R2 (Pseudo R2)(0.0344)(0.0233)−0.05−0.00

The median regression estimation result in Column (2) implies that when EM is weak, the median pay sensitivity for the CEO with the most abnormal power is lower than that for the CEO with the least abnormal power by $0.164 per $1000 change in shareholder value. Considering that the PPS for the CEO with the least power is $0.497 per $1000 change in shareholder value, the $0.164 decrease represents a 33% decrease in PPS.

Our estimated pay sensitivities are smaller than those reported by Milbourn (2003) and Rajgopal et al. (2006), who use similar empirical methodology. The difference is in the dependent variable. Their dependent variable includes changes in the value of all stocks and stock options held by CEOs and, hence, measures CEOs’ firm-related wealth change, whereas our dependent variable includes changes only in compensation flows. When Hartzell and Starks (2003) estimate executives’ PPS of salaries and bonuses, they show only $0.032 per $1000 change in shareholder value. Our estimates are much greater than theirs because we include the value of new grants of stocks and stock options in our measure of CEO compensation flows.

5. Firm Performance

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Hypotheses Development
  5. 3. Empirical Design and Data
  6. 4. Pay for Performance Sensitivity
  7. 5. Firm Performance
  8. 6. Robustness
  9. 7. Conclusions
  10. References

How does the effect of abnormal CEO power on the compensation process show up on the bottom line? If a CEO uses the power to capture the compensation process and lowers his or her PPS, the alignment of his or her incentive to shareholder value is reduced to below the optimal level. Such deviations from optimal incentive contracts might hurt firm performance. In this section we test this hypothesis by examining how firm performance is related to abnormal CEO power.

The relation between firm performance and abnormal CEO power is estimated with the following specification:

  • image(3)

where Performit is measured by either Tobin’s Q or ROA. Tobin’s Q is equal to the book value of assets plus the market value of common stock minus the sum of book value of common stock divided by the book value of assets. ROA is return on assets. Summary statistics on Q and ROA are reported in Table 4.

The control variables include the standard controls used in Q regressions (e.g. Himmelberg et al., 1999; Kim and Lu, 2011), plus variables measuring CEO characteristics. We control for firm size, measured by the log of sales (LNS);9 growth and discretionary investment opportunities, measured by the ratio of capital expenditures to property, plant, and equipment (I/K); tangibility of assets, measured by the ratio of property, plant, and equipment to sales (K/S); firm age, measured as the log of one plus the number of years from a firm’s initial public offerings as reported in CRSP or the number of years since its first appearance in CRSP (FirmAge); an indicator for CEO gender (Female); CEO age (CEOAge); and an indicator for a CEO chairing the board (CEO_Chair). In addition, we control for firm and year fixed effects. All regressions are estimated using pooled panel data with robust standard errors.

5.1. Full Sample Results

Table 7 reports regression estimates for Tobin’s Q and ROA. The results are consistent with those for PPS. Firm performance, whether measured by Tobin’s Q or ROA, is negatively related to the measure of abnormal CEO power. The coefficients on Power are negative and significant for both Q and ROA. The coefficients imply that a one standard deviation increase in the abnormal power is associated with a 0.136% lower Tobin’s Q and a 0.485% lower ROA.

Table 7.   Relation between chief executive officer (CEO) power and firm performance Regression in column (1) estimates the relation CEO power and Tobin’s Q with the full sample over the entire sample period. Regression in column (2) estimates the relation between CEO power and ROA. Definitions of all variables are given in Table 1. All regressions control for firm- and year fixed effects. Robust standard errors are reported in parentheses. Coefficients marked with ***, **, and * are significant at 1, 5, and 10%, respectively.
 Tobin’s Q (1)ROA (2)
Power−1.286*** (0.302)−4.575** (1.800)
FirmAge−0.521*** (0.076)−2.293*** (0.454)
LNS−0.917*** (0.061)2.738*** (0.367)
K/S−0.033*** (0.009)0.045 (0.053)
I/K5.716*** (0.291)0.792 (1.738)
Female0.188 (0.328)−1.735 (1.956)
CEOAge−0.592** (0.258)−0.680 (1.541)
CEO_Chair0.165* (0.087)0.542 (0.519)
Constant11.100*** (1.074)−4.432 (6.410)
Firm fixed effects and year fixed effects Y Y
Observations98559855
Adjusted R20.390.40

Control variables indicate that Q is negatively correlated with size, firm age, tangibility of assets, and CEO age, but positively correlated with I/K. All of these results are consistent with previous studies (e.g., Himmelberg et al., 1999; Kim and Lu, 2011). ROA is also negatively related to firm age, but, unlike Q, is positively related to size.

5.2. Interactive Effects with External Monitoring

Because the effects of abnormal CEO power on PPS depend on the strength of EM, if abnormal power indeed affects firm performance, the performance effect should also depend on EM. That is, the negative effects on firm performance should be most pronounced when EM is too weak to deter the negative effects of abnormal power. (See Durnev and Kim (2005) for interactive effects of internal and external governance in a cross-country context.) To test this hypothesis, we estimate the interactive effects of EM and abnormal power on firm performance.

Table 8 reports the estimation results. Columns (1), (2), (4), and (5) contain separate estimation results for strong and weak EM subsamples for Q or ROA. The results are remarkably consistent with the PPS results. Abnormal power and firm performance are unrelated for observations under strong EM. The negative relation for the full sample is driven by observations under weak EM. This is true regardless of whether performance is measured by Tobin’s Q (Column 2) or ROA (Column 5). The estimated coefficients for the low IOC sample indicate that one standard deviation increase in the abnormal CEO power is associated with a 0.342% decrease in Tobin’s Q and a 1.216% decrease in ROA.

Table 8.   Relation between chief executive officer (CEO) power and firm performance: high and low institutional ownership concentration This table reports regression estimates separately for firm year observations with high and low institutional ownership concentration (IOC). Columns (1) and (4) report results estimated for the HIOC subsample, columns (2) and (5) report results estimated for the LIOC subsample. Columns (3) and (6) report the results estimated for the full sample with the interaction term between IOC and CEO power. HIOC and LIOC are defined as above and below the sample median IOC. The dependent variable is Tobin’s Q in columns (1)–(3) or ROA in columns (4)–(6). Definitions of all variables are given in Table 1. All regressions control for firm- and year fixed effects. Robust standard errors are reported in parentheses. Coefficients marked with ***, **, and * are significant at 1, 5, and 10%, respectively.
 Tobin’s QROA
HIOC (1)LIOC (2)Full (3)HIOC (4)LIOC (5)Full (6)
Power0.122 (0.283)−3.222*** (0.695)−6.730*** (0.780)−1.620 (1.610)−11.467*** (4.057)−17.036*** (4.521)
Power × IOC  20.296*** (2.725)  50.308*** (15.804)
IOC  −1.743*** (0.380)  0.367 (2.203)
FirmAge−0.189** (0.081)−1.815*** (0.235)−0.662*** (0.095)0.525 (0.459)−1.527 (1.372)−1.304** (0.550)
LNS−0.196*** (0.068)−1.860*** (0.147)−1.051*** (0.071)2.805*** (0.386)2.216*** (0.858)2.851*** (0.413)
K/S−0.272*** (0.074)−0.062*** (0.013)−0.041*** (0.010)−3.506*** (0.421)0.316*** (0.076)0.284*** (0.055)
I/K4.105*** (0.335)6.075*** (0.527)5.847*** (0.312)9.160*** (1.908)−5.996* (3.078)0.658 (1.806)
Female−0.121 (0.305)0.594 (0.964)0.177 (0.387)−0.498 (1.733)−1.047 (5.627)−0.623 (2.246)
CEOAge−0.503** (0.250)−0.472 (0.583)−0.584** (0.285)1.864 (1.421)−3.958 (3.404)−0.506 (1.653)
CEO_Chair0.126 (0.084)−0.054 (0.197)0.144 (0.095)1.544*** (0.478)−1.092 (1.149)0.519 (0.553)
Constant5.385*** (1.059)21.464*** (2.404)12.758*** (1.186)−22.016*** (6.020)11.471 (14.035)−8.761 (6.879)
Firm fixed effects and year fixed effectsYYY Y YY
Observations475742218978475742218978
Adjusted R20.520.360.400.430.430.43

Columns (3) and (6) utilize the full sample with an interaction term between the abnormal power and IOC. This approach has the benefit of fully utilizing the information in the IOC variable, but at the cost of forcing the coefficients of other independent variables to be the same for observations under both strong and weak EM. The estimation result is robust to this alternative specification. The interaction term is positive and significant, regardless of whether performance is measured by Q or ROA, implying that stronger monitoring by institutional investors negates the negative effects that abnormal CEO power has on firm performance.10

6. Robustness

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Hypotheses Development
  5. 3. Empirical Design and Data
  6. 4. Pay for Performance Sensitivity
  7. 5. Firm Performance
  8. 6. Robustness
  9. 7. Conclusions
  10. References

In this section we address endogenous issues. There are alternative explanations for our results. For one, poorly performing firms might give their CEOs’ more abnormal power to make it easier to implement changes, causing a negative correlation between the power variable and firm performance. However, this does not explain why the negative relation is observed only when EM is weak. When EM is strong, the external monitors are more likely to be aware of the need for stronger leadership to implement changes, predicting a stronger negative correlation, which is contradicted by our evidence of no relation under strong EM.

Another possible scenario is that when a firm performs poorly, more top executives leave, opening more positions for the remaining CEO to fill, thereby increasing the abnormal fraction of executives hired during the current CEO’s tenure.11 This story also does not explain why it does not work for observations under strong EM. If anything, external monitors under strong EM are more likely to demand a shakeup in executive suites, leading to more replacement and more new hiring.

Finally, IOC might not be completely exogenous. It could be affected by the abnormal CEO power, Q, or ROA. For example, institutional investors might be more attracted to firms with an appropriate level of CEO power, better performing firms, or firms undervalued by the market. To address these potential issues, we estimate three-stage least squares simultaneous regressions as follows:

  • image(4)
  • image(5)
  • image(6)

In this system of equations, the performance variables, the power variable, and IOC are all treated as endogenous. We estimate the system of equations with the full sample using the interaction terms of the power variable and IOC to estimate the interactive effects of the power and the strength of EM. In the performance equation, we include the power variable, IOC, and all control variables measuring firm characteristics and CEO characteristics used in equation (3). In the CEO power equation, independent variables include IOC, the performance variable, FirmAgeit, Femaleit, CEOAgeit, and CEO_Chairit, but not K/S and I/K, because the abnormal CEO power might be related to CEO characteristics and some basic firm characteristics, such as firm size and firm age, but might be unrelated to the tangibility of assets or growth and discretionary investment opportunities. In the IOC equation, independent variables include CEO power, a performance variable, FirmAgeit, K/Sit, and I/Kit, but not Femaleit, CEOAgeit, and CEO_Chair. Although IOC might be affected by firm characteristics, it is unlikely to be affected by CEO characteristics. Additionally, we include a predictor of IOC in the IOC equation: IOC of peer firms, IOC_IY(−i)it, the mean value of IOC of all firms in the same industry in the same year, excluding the firm itself. A firm’s IOC might be affected by how much institutional investors are attracted to other firms in the same industry during the same year. All regressions in the system include firm and year fixed effects.

Table 9 presents the estimation results. They show no relation between power and IOC; however, the results do not rule out the possibility that firm performance affects the power measure or IOC. Nevertheless, column (1) demonstrates the robustness of our conclusions regarding the relation between Q and power: CEO power has a negative effect on firm value and the negative effect is deterred by high IOC. The same cannot be said about the relation between ROA and power. ROA is based on accounting numbers; as such, a CEO with powerful influence over his or her top executives is in a stronger position to smooth out fluctuations in ROA. We suspect the weak relation between power and ROA is partly due to earnings management. Because Q is less subject to such “management” and is a more direct market-based measure of shareholder value, we conclude that abnormal CEO power is bad for shareholder value when external monitoring is not strong enough to restrain it.

Table 9.   Three-stage least squares estimation of firm performance, chief executive officer (CEO) power, and institutional ownership concentration Regressions in columns (1)–(3) estimate the equations for Tobin’s Q, CEO power, and IOC. Regressions in columns (4)–(6) estimate the equations for ROA, CEO power, and IOC. Definitions of all variables are given in Table 1. All regressions control for industry- and year-fixed effects. Industries are defined as in Fama and French (1997). Robust standard errors are reported in parentheses. Coefficients marked with ***, **, and * are significant at 1, 5, and 10%, respectively.
 3 SLS of Tobin’s Q regressions3 SLS of ROA regressions
Tobin’s Q (1)Power (2)IOC (3)ROA (4)Power (5)IOC (6)
Power−4.079*** (0.718) −0.0112 (0.00937)−6.878 (4.586) −0.00379 (0.00941)
Power × IOC11.52*** (2.564)  19.61 (16.38)  
IOC−5.802*** (0.282)−0.0177 (0.0121) 19.59*** (1.799)−0.00764 (0.0121) 
Tobin’s Q −0.00177*** (0.000441)−0.00820*** (0.000394)   
ROA    −8.47e-05 (7.12e-05)0.000672*** (6.30e-05)
FirmAge−0.236*** (0.0369)0.00173 (0.00155)−0.0103*** (0.00137)−0.0777 (0.235)0.00242 (0.00154)−0.00854*** (0.00138)
LNS−0.104*** (0.0205)−0.00155* (0.000865)−0.00739*** (0.000759)2.065*** (0.130)−0.00115 (0.000879)−0.00813*** (0.000772)
K/S−0.00386 (0.00877) −0.00111*** (0.000329)−0.643*** (0.0558) −0.000668** (0.000333)
I/K6.814*** (0.290) 0.00325 (0.0112)3.884** (1.843) −0.0575*** (0.0109)
Female1.076*** (0.241)0.00598 (0.0104) 3.829** (1.542)0.00438 (0.0104) 
CEOAge−0.752*** (0.206)−0.0391*** (0.00882) 5.237*** (1.314)−0.0368*** (0.00882) 
CEO_Chair0.136** (0.0590)−0.000229 (0.00254) −0.278 (0.377)−0.000509 (0.00254) 
IOC_IY(−i)  −0.333*** (0.0606)  −0.331*** (0.0612)
Constant 0.158*** (0.0536) −33.47*** (8.085)0.0600 (0.0541) 
Observations877487748774877487748774
Industry fixed effects and fixed effectsYYYYYY
χ29661.94191.6569 292.46997.26176.6168 336.96
R20.1890.0200.1610.0910.0200.160

7. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Hypotheses Development
  5. 3. Empirical Design and Data
  6. 4. Pay for Performance Sensitivity
  7. 5. Firm Performance
  8. 6. Robustness
  9. 7. Conclusions
  10. References

This paper focuses on CEO structural power, as measured by an abnormal fraction of top executive hired by the current CEO. The abnormal CEO power seems to decrease CEOs’ pay for performance sensitivity and to reduce firm performance. These results not only suggest that CEOs’ abnormal power is bad for firm performance but also illustrate that using abnormal power to capture the compensation process is a channel through which CEO power affects firm performance. These results are robust to firm fixed characteristics and potential reverse causality.

The negative effects of CEO power on CEO PPS and firm performance are observed only when monitoring by institutional investors is weak. CEO power seems benign when they are subject to close monitoring by institutional investors. Proper monitoring and guidance by institutional investors appear effective in preventing CEOs from using their power to capture the compensation process and hurting firm performance. Although unchecked CEO power is dangerous, it can be benign when sufficient checks and balances against potential abuse of the power exist.

Footnotes
  • 1

    Ownership power stems from equity ownership. In another paper, we analyze how CEO ownership affects firm performance and risk-taking (Kim and Lu, 2011). Expert power arises from the ability to contribute to organizational success by influencing a particular strategic choice through functional expertise. Prestige power represents personal prestige, status of reputation, and others’ perception of CEO influence through contacts and qualifications. Expert and prestige power are similar in that both arise from personal abilities to make contributions to firm performance. These ability-based powers are unlikely to harm firm performance. In an attempt to measure ability-based powers, Milbourn (2003) and Rajgopal et al. (2006) use the number of times a CEO is mentioned in the press as a proxy for CEO reputation and outside employment opportunity.

  • 2

    Managerial power has been a subject of much research in the management literature. See Adams et al. (2005) for a brief review from the finance/governance perspective.

  • 3

    Previous studies relying on ExecuComp to obtain CEO tenure report non-trivial numbers of negative CEO tenure. We trace the negative tenure to the practice of ExecuComp reporting a CEO starting year only for the latest appointment. Thus, if a CEO leaves the position and returns later, relying on an ExecuComp start date will give a negative tenure. We correct for this problem by backtracking the previous appointment year using the CEO name.

  • 4

    Other proxies used to estimate CEOs’ structural power include CEO centrality as measured by the pay gap between CEO and other top executives (Bebchuk et al., 2008; Morse et al., 2010) and CEO being the only inside member of the board (Adams et al., 2005; Bebchuk et al., 2008). We do not use these proxies because they are multidimensional. For CEO centrality, the contracting view suggests that compensation differences between a CEO and his or her top executives should reflect not only the concentration of his or her structural power but also differences in expert and prestige power, which is likely to have a favorable impact on firm performance. As for the CEO being the only member of the board, it has a duality: the CEO might have more structural power over other executives, but he might have less influence over the board because it has more outside directors. Finally, CEO tenure is not used as a proxy for CEO power because it contains both structural power obtained over time and ability-based power as reflected by the ability to hold on to the job.

  • 5

    We use a pooled sample median as the demarcation point rather than the yearly median because of the time trend. IOC has been increasing steadily over time: the mean IOC is 18% in 1992, 24% in 1999, and 30% in 2006. Therefore, classifying 2006 firm observations into high and low IOC by the 2006 median might wrongly classify a firm-year into low IOC when it has a high IOC relative to the entire sample.

  • 6

    We also exclude observations with negative market-to-book value ratios.

  • 7

    Stata does not allow estimation of median regressions with firm fixed effects. OLS mean regressions are estimated to check the robustness to firm fixed effects.

  • 8

    See Koenker and Hallock (2001) for more in-depth discussion on quantile regression.

  • 9

    We do not measure firm size by total assets in performance regressions because the calculations of both Tobin’s Q and ROA include total assets.

  • 10

    We do not use this interaction term approach for the PPS analysis because it would require several triple interaction terms in a single regression, causing a rather severe multicollinearity problem. It would also make it difficult to interpret the coefficients.

  • 11

    This possibility was raised by Landier et al. (2008) but was rejected as an explanation for a negative relation between the abnormal power and Q.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Hypotheses Development
  5. 3. Empirical Design and Data
  6. 4. Pay for Performance Sensitivity
  7. 5. Firm Performance
  8. 6. Robustness
  9. 7. Conclusions
  10. References
  • Adams, R. B., H. Almeida, and D. Ferreira, 2005, Powerful CEOs and their impact on corporate performance, Review of Financial Studies 18, pp. 14031432.
  • Aggarwal, R. K., and A. A. Samwick, 1999, The other side of the trade-off: The impact of risk on executive compensation, Journal of Political Economy 107, pp. 65105.
  • Baker, G. P., and B. J. Hall, 2004, CEO incentives and firm size, Journal of Labor Economics 22, pp. 767798.
  • Bebchuk, L., and J. Fried, 2004, Pay without performance: The unfulfilled promise of executive compensation (Harvard University Press, Cambridge, MA.).
  • Bebchuk, L. A., K. J. M. Cremers, and U. C. Peyer, 2011, The CEO pay slice. Journal of Financial Economics (forthcoming).
  • Bennedsen, M., F. Perez-Gonzalez, and D. Wolfenzon, 2006, Do CEOs matter? Working Paper, Columbia University, New York.
  • Bertrand, M., and S. Mullainathan, 2000, Agents with and without principals, American Economics Review 90, pp. 203208.
  • Bertrand, M., and S. Mullainathan, 2001, Are CEOs rewarded for luck? The ones without principals are, Quarterly Journal of Economics 116, pp. 901932.
  • Bertrand, M., and A. Schoar, 2003, Managing with style: The effect of managers on firm policies, Quarterly Journal of Economics 118, pp. 11691208.
  • Cremers, K. J. M., and V. B. Nair, 2005, Governance mechanisms and equity prices, Journal of Finance 60, pp. 28592894.
  • Cronqvist, H., A. K. Makhija, and S. E. Yonker, 2009, What does CEOs’ personal leverage tell us about corporate leverage? Working Paper Series 2009-4, Ohio State University, Charles A. Dice Center for Research in Financial Economics, Columbus, OH.
  • Crystal, G., 1991, In search of excess: The overcompensation of American executives (W.W. Norton, New York).
  • Del Guercio, D., L. Seery, and T. Woidtke, 2008, Do boards pay attention when institutional investor activists “just vote no”? Journal of Financial Economics 90, pp. 84103.
  • Durnev, A., and E. H. Kim, 2005, To steal or not to steal: Firm attributes, legal environment, and valuation, Journal of Finance 60, pp. 14611495.
  • Edmans, A., 2009. Blockholder trading, market efficiency, and managerial myopia, Journal of Finance 64, pp. 24812514.
  • Fama, E., and K. French, 1997, Industry costs of equity, Journal of Financial Economics 43, pp. 153193.
  • Finkelstein, S., 1992, Power in top management teams: Dimensions, measurement, and validation, Academy of Management Journal 35, pp. 505538.
  • Gibbons, R., and K. Murphy, 1992, Optimal incentive contracts in the presence of career concerns: Theory and evidence, Journal of Political Economy 100, pp. 468505.
  • Hall, B. J., and J. B. Liebman, 1998, Are CEOs really paid like bureaucrats? Quarterly Journal of Economics 111, pp. 653691.
  • Hartzell, J., and L. T. Starks, 2003, Institutional investors and executive compensation, Journal of Finance 58, pp. 23512374.
  • Himmelberg, C. P., R. G. Hubbard, and P. Darius, 1999, Understanding the determinants of managerial ownership and the link between ownership and performance, Journal of Financial Economics 53, pp. 353384.
  • Jenter, D., and K. Lewellen, 2011, CEO preferences and acquisitions, Working Paper, Stanford University, Palo Alto, CA.
  • Kim, E. H., and Y. Lu, 2011, CEO ownership, external governance, and risk-taking, Journal of Financial Economics (forthcoming).
  • Koenker, R., and K. F. Hallock, 2001, Quantile regression, Journal of Economic Perspectives 15, pp. 143156.
  • Lafontaine, F., and S. Bhattacharyya, 1995, The role of risk in franchising, Journal of Corporate Finance 2, pp. 3974.
  • Landier, A., D. Sraer, and D. Thesmar, 2008, Bottom-up corporate governance, NYU Working Paper No. FIN-05-011, New York, NY. Available at SSRN: http://ssrn.com/abstract=1294147
  • Milbourn, T. T., 2003, CEO reputation and stock-based compensation, Journal of Financial Economics 68, pp. 233263.
  • Morse, A., V. Nanda, and A. Seru, 2010, Are incentive contracts rigged by powerful CEOs? Journal of Finance (forthcoming).
  • Murphy, K. J., 1999, Executive compensation, In O.Ashenfelter, D.Card eds: Handbook of labor economics, Chapter 38 (Elsevier Science B. V., Amsterdam), pp. 24852563.
  • Nguyen, B. D., and K. M. Nielsen, 2010, What death can tell: Are executives paid for their contributions to firm value? Working Paper, Chinese University of Hong Kong, Hong Kong.
  • Prendergast, C., 2002, The tenuous trade-off between risk and incentives, Journal of Political Economy 110, pp. 10711102.
  • Rajgopal, S., T. Shevlin, and V. Zamora, 2006, CEOs’ outside employment opportunities and the lack of relative performance evaluation in compensation contracts, Journal of Finance 61, pp. 18131844.
  • Schaefer, S., 1998, The dependence of pay-performance sensitivity on the size of the firm, Review of Economics and Statistics 80, pp. 436443.
  • Shleifer, A., and R. Vishny, 1986, Large shareholders and corporate control, Journal of Political Economy 94, pp. 461488.